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Data science is a vast field, combining elements of statistics, machine learning, and data analysis. To navigate this complex domain, having a set of handy cheat sheets can be immensely helpful.
The cheat sheets can also serve as a valuable resource for preparing for technical interviews, reviewing key concepts, and providing an overview for beginners starting their careers in data science.
Here are five super cheat sheets that every data science professional and enthusiast should have:
Link: Data-Science-Cheatsheet/data-science-cheatsheet.pdf
This comprehensive 9-page reference covers the basics of probability, statistics, statistical learning, machine learning, big data frameworks, and SQL. Ideal for those with a basic understanding of statistics and linear algebra, it’s a great starting point for anyone diving into data science.
Link: CME 106 (stanford.edu)
This cheat sheet is a concise summary of key concepts in probability and statistics. It includes topics like random samples, estimators, the Central Limit Theorem, confidence intervals, hypothesis testing, regression analysis, correlation coefficients, and more. It’s perfect for understanding the foundational statistical concepts that are crucial in data science.
Link: aaronwangy/Data-Science-Cheatsheet
This cheat sheet is a condensed version of data science knowledge, encompassing over a semester’s worth of introductory machine learning based on MIT’s Machine Learning courses 6.867 and 15.072. It covers topics such as linear and logistic regression, decision trees, SVM, K-Nearest Neighbors, and more. The cheat sheet is a valuable resource for exam reviews, interview preparation, and a quick refresher on key machine learning concepts.
Link: afshinea/stanford-cs-229-machine-learning
This cheat sheet summarizes the key concepts covered in Stanford’s CS 229 Machine Learning course. It includes refreshers on related topics (Probabilities and Statistics, Algebra, and Calculus), detailed cheat sheets for each machine learning field, and an ultimate compilation of important concepts. It’s an essential resource for anyone interested in delving deeper into machine learning. It’s designed for experts and provides a quick reference for basic concepts.
Link: afshinea/stanford-cs-230-deep-learning
If you’re interested in deep learning, the CS 230 course from Stanford has an excellent collection of materials that cover everything you need to know about convolutional neural networks and recurrent neural networks and offers tips for training deep learning models. This resource is invaluable for anyone focusing on the deep learning aspect of data science, and it is FREE.
These cheat sheets offer a concise and effective way to review and strengthen your understanding across data science disciplines. From the basics of statistics to the intricacies of machine learning and deep learning, these resources are invaluable for students, professionals, and enthusiasts alike. Refer to them often to solidify foundational concepts or brush up on the latest methodologies.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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